Quality-sensitive applications of machine learning (ML) require quality assurance (QA) by humans before the predictions of an ML model can be deployed. QA for ML (QA4ML) interfaces require users to view a large amount of data and perform many interactions to correct errors made by the ML model. An optimized user interface (UI) can significantly reduce interaction costs. While UI optimization can be informed by user studies evaluating design options, this approach is not scalable, because there are typically numerous small variations that can affect the efficiency of a QA4ML interface. Hence, we propose using simulation to evaluate and aid the optimization of QA4ML interfaces. In particular, we focus on simulating the combined effects of human intelligence in initiating appropriate interaction commands and machine intelligence in providing algorithmic assistance for accelerating QA4ML processes. As QA4ML is usually labor-intensive, we use the simulated task completion time as the metric for UI optimization under different interface and algorithm setups. We demonstrate the usage of this UI design method in several QA4ML applications.

Icicles and sunbursts are two commonly-used visual representations of trees. While icicle trees can map data values faithfully to rectangles of different sizes, often some rectangles are too narrow to be noticed easily. When an icicle tree is transformed into a sunburst tree, the width of each rectangle becomes the length of an annular sector that is usually longer than the original width. While sunburst trees alleviate the problem of narrow rectangles in icicle trees, it no longer maintains the consistency of size encoding. At different tree depths, nodes of the same data values are displayed in annular sections of different sizes in a sunburst tree, though they are represented by rectangles of the same size in an icicle tree. Furthermore, two nodes from different subtrees could sometimes appear as a single node in both icicle trees and sunburst trees. In this paper, we propose a new visual representation, referred to as radial icicle tree (RIT), which transforms the rectangular bounding box of an icicle tree into a circle, circular sector, or annular sector while introducing gaps between nodes and maintaining area constancy for nodes of the same size. We applied the new visual design to several datasets. Both the analytical design process and user-centered evaluation have confirmed that this new design has improved the design of icicles and sunburst trees without introducing any relative demerit.